title: A variational nonparametric Bayesian approach for inferring rat hippocampal population codes.
abstract: Rodent hippocampal population codes represent important spatial information of the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes. Specifically, we develop an infinite hidden Markov model (iHMM) and variational Bayes (VB) inference method to analyze rat hippocampal ensemble spike activity. We demonstrate the effectiveness of our approach using an open field navigation example and discuss the significance/implications of our results.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Wilson
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D006624:Hippocampus / Q000502:physiology*; D008124:Locomotion; D008297:Male; D008390:Markov Chains; D008959:Models, Neurological; D051381:Rats; D020318:Rats, Long-Evans